Brain
Contents
Brain#
The results for the CNeuroMod quantitative MRI longitudinal stability study for the brain are displayed below in two sections, Quantitative MRI and Diffusion, reflecting the two separate pipelines used to processes these datasets (see the Materials and Methods section for pipeline diagrams). The mean intrasubject and intersubject COVs are reported in tables below each respective figure, as well as the intrasubject COV standard deviation.
The figures are presented in an interactive format using the Plotly framework, you can hover to view the values of each datapoints, use the dropdown box (when applicable) to change between metrics, click and drag to zoom in, etc.
This page was generated using an Jupyter Notebook, and all the commands run to reproduce the figure using the prepared and packaged ROI data are shown prior to the figures. If you’d like to re-run the notebook, you can click the 🚀 icon on the top right of this page and then on “Binder” to open a MyBinder session in your browser - no installation is required.
Quantitative MRI#
Code imports#
# Python imports
from IPython.display import clear_output
from pathlib import Path
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.colheader_justify', 'center')
pd.set_option('display.precision', 1)
# Import custom tools
from tools.data import Data
from tools.plot import Plot
from tools.stats import Stats
Download data#
data_type = 'brain'
release_version = 'latest'
dataset = Data(data_type)
dataset.download(release_version)
--2023-05-02 00:32:36-- https://github.com/courtois-neuromod/anat-processing/releases/download/r20220921/neuromod-anat-brain-qmri.zip
Resolving github.com (github.com)... 140.82.112.4
Connecting to github.com (github.com)|140.82.112.4|:443... connected.
HTTP request sent, awaiting response...
302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/59a68bb3-4423-49ab-959d-247690acbebc?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230502%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230502T003237Z&X-Amz-Expires=300&X-Amz-Signature=762c746c0d8026320470190f64a9b7ece11329ce25e422dc64b6d9538ceb6847&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dneuromod-anat-brain-qmri.zip&response-content-type=application%2Foctet-stream [following]
--2023-05-02 00:32:37-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/59a68bb3-4423-49ab-959d-247690acbebc?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230502%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230502T003237Z&X-Amz-Expires=300&X-Amz-Signature=762c746c0d8026320470190f64a9b7ece11329ce25e422dc64b6d9538ceb6847&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dneuromod-anat-brain-qmri.zip&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1301347 (1.2M) [application/octet-stream]
Saving to: ‘neuromod-anat-brain-qmri.zip’
0K .......... .......... .......... .......... .......... 3% 5.94M 0s
50K .......... .......... .......... .......... .......... 7% 6.28M 0s
100K .......... .......... .......... .......... .......... 11% 51.2M 0s
150K .......... .......... .......... .......... .......... 15% 25.5M 0s
200K .......... .......... .......... .......... .......... 19% 9.61M 0s
250K .......... .......... .......... .......... .......... 23% 44.9M 0s
300K .......... .......... .......... .......... .......... 27% 141M 0s
350K .......... .......... .......... .......... .......... 31% 52.3M 0s
400K .......... .......... .......... .......... .......... 35% 31.4M 0s
450K .......... .......... .......... .......... .......... 39% 143M 0s
500K .......... .......... .......... .......... .......... 43% 12.3M 0s
550K .......... .......... .......... .......... .......... 47% 101M 0s
600K .......... .......... .......... .......... .......... 51% 24.4M 0s
650K .......... .......... .......... .......... .......... 55% 101M 0s
700K .......... .......... .......... .......... .......... 59% 125M 0s
750K .......... .......... .......... .......... .......... 62% 101M 0s
800K .......... .......... .......... .......... .......... 66% 112M 0s
850K .......... .......... .......... .......... .......... 70% 121M 0s
900K .......... .......... .......... .......... .......... 74% 146M 0s
950K .......... .......... .......... .......... .......... 78% 162M 0s
1000K .......... .......... .......... .......... .......... 82% 381M 0s
1050K .......... .......... .......... .......... .......... 86% 18.2M 0s
1100K .......... .......... .......... .......... .......... 90% 245M 0s
1150K .......... .......... .......... .......... .......... 94% 257M 0s
1200K .......... .......... .......... .......... .......... 98% 26.9M 0s
1250K .......... .......... 100% 416M=0.04s
2023-05-02 00:32:37 (29.2 MB/s) - ‘neuromod-anat-brain-qmri.zip’ saved [1301347/1301347]
Load data plot it#
qMRI Metrics#
dataset.load()
fig_gm = Plot(dataset, plot_name = 'brain-1')
fig_gm.title = 'Brain qMRI microstructure measures'
# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_gm.display('jupyter-book')
Statistics#
White Matter#
stats_wm = Stats(dataset)
stats_wm.build_df('WM')
stats_wm.build_stats_table()
display(stats_wm.stats_table)
| T1 (MP2RAGE) | T1 (MTsat) | MTR | MTsat | |
|---|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 2.3 | 0.6 | 1.7 |
| intrasubject COV std [%] | 0.2 | 0.8 | 0.1 | 0.5 |
| intersubject mean COV [%] | 1.9 | 3.5 | 0.4 | 2.2 |
Grey Matter#
stats_gm = Stats(dataset)
stats_gm.build_df('GM')
stats_gm.build_stats_table()
display(stats_gm.stats_table)
| T1 (MP2RAGE) | T1 (MTsat) | MTR | MTsat | |
|---|---|---|---|---|
| intrasubject COV mean [%] | 0.4 | 3.1 | 0.8 | 2.7 |
| intrasubject COV std [%] | 0.1 | 1.6 | 0.2 | 1.2 |
| intersubject mean COV [%] | 1.0 | 5.7 | 1.2 | 4.5 |
Diffusion#
data_type = 'brain-diffusion-cc'
release_version = 'latest'
dataset = Data(data_type)
dataset.download(release_version)
--2023-05-02 00:32:39-- https://github.com/courtois-neuromod/anat-processing/releases/download/r20230110/brain-diffusion-cc.zip
Resolving github.com (github.com)... 140.82.112.4
Connecting to github.com (github.com)|140.82.112.4|:443... connected.
HTTP request sent, awaiting response...
302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/6e6dd34d-c009-4079-bea8-df5eea106c89?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230502%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230502T003239Z&X-Amz-Expires=300&X-Amz-Signature=c6ffb3f50829939965d867ec7252273647b09a04bf26b5570ef76cc07d55fa7c&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dbrain-diffusion-cc.zip&response-content-type=application%2Foctet-stream [following]
--2023-05-02 00:32:39-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/333825187/6e6dd34d-c009-4079-bea8-df5eea106c89?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230502%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230502T003239Z&X-Amz-Expires=300&X-Amz-Signature=c6ffb3f50829939965d867ec7252273647b09a04bf26b5570ef76cc07d55fa7c&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=333825187&response-content-disposition=attachment%3B%20filename%3Dbrain-diffusion-cc.zip&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.111.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 15248 (15K) [application/octet-stream]
Saving to: ‘brain-diffusion-cc.zip’
0K .......... .... 100% 27.8M=0.001s
2023-05-02 00:32:39 (27.8 MB/s) - ‘brain-diffusion-cc.zip’ saved [15248/15248]
dataset.load()
fig_diff = Plot(dataset, plot_name = 'brain-diff-cc')
fig_diff.title = 'Brain diffusion measures (corpus callosum)'
# If you're running this notebook in a Jupyter Notebook (eg, on MyBinder), change 'jupyter-book' to 'notebook'
fig_diff.display('jupyter-book')
Statistics#
Genu#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('genu')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.8 | 1.0 | 1.3 |
| intrasubject COV std [%] | 0.3 | 0.6 | 0.6 |
| intersubject mean COV [%] | 4.2 | 6.2 | 10.3 |
Body#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('body')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 0.7 | 0.7 |
| intrasubject COV std [%] | 0.2 | 0.2 | 0.3 |
| intersubject mean COV [%] | 3.8 | 3.0 | 6.2 |
Splenium#
stats_cc1 = Stats(dataset)
stats_cc1.build_df('splenium')
stats_cc1.build_stats_table()
display(stats_cc1.stats_table)
| FA (DWI) | MD (DWI) | RD (DWI) | |
|---|---|---|---|
| intrasubject COV mean [%] | 0.6 | 0.7 | 0.8 |
| intrasubject COV std [%] | 0.1 | 0.2 | 0.3 |
| intersubject mean COV [%] | 2.6 | 3.1 | 6.3 |